Improved anomaly diagnosis of production facilities by combining Autoencoder with spectral characteristics
In a previous study, we had proposed a method for detecting abnormalities using an Autoencoder. It considers the amplitude value of each frequency spectrum while performing the FFT analysis of time-series data. In this study, we applied this method to detect abnormalities in pressure washers. In par...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | Japanese |
| Published: |
The Japan Society of Mechanical Engineers
2025-03-01
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| Series: | Nihon Kikai Gakkai ronbunshu |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/transjsme/91/944/91_24-00161/_pdf/-char/en |
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| Summary: | In a previous study, we had proposed a method for detecting abnormalities using an Autoencoder. It considers the amplitude value of each frequency spectrum while performing the FFT analysis of time-series data. In this study, we applied this method to detect abnormalities in pressure washers. In particular, we verified its detection accuracy on the artificially-generated anomaly data. The results showed a deteriorated detection performance for a varying spectrum amplitude near the resonance frequency. Therefore, in addition to the conventional autoencoder, the proposed method further improves anomaly detection accuracy by treating the spectrum as a lumped spectrum in a predetermined frequency range. The effectiveness of the proposed method was verified using data obtained from equipment anomalies. In conclusion, the proposed anomaly-detection method can robustly cope with frequency fluctuations near the peaks and detect anomalies with a accuracy higher than that of the conventional anomaly detection method, which uses only an autoencoder. Thus, the proposed method can detect anomalies even before factory become aware of them. |
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| ISSN: | 2187-9761 |